Description
Uncertainty quantification (UQ) methods enable us to equip machine learning model predictions with "error bars". These are useful in many areas from active learning to out-of-distribution detection and in general to tackle trustworthiness issues (think driving, health, legal applications).
We'll also touch on recent methods to make generative models (hello ChatGPT) express confidence in their answers (i.e. is the answer likely to be true or a hallucination?).
We'll give an introduction to the main ideas and most common methods, followed by a relaxed open discussion.
This won't be a talk or hands-on workshop, but rather a casual meetup for people who apply and/or are interested in uncertainty quantification (UQ) for machine learning models. Whether you are an expert or have never heard of UQ, let's talk!
List of notes and papers from a previous version of this format: https://github.com/elcorto/37c3_uq_meetup
